The present invention relates generally to gas turbine systems and more specifically to methods and apparatus for monitoring performance of gas turbine systems.
A gas turbine typically comprises a compressor for compressing air and a combustor where the compressed air from the compressor and gas fuel are mixed and burned. The hot gases from the combustor drive the turbine stages to generate power. Normally, for installed turbines, performance monitoring is done through daily checks and measurements and periodic performance tests. The results are later used for maintenance and repair diagnostic processes. For example, after a fault occurs the previously recorded trends of the machine are analyzed to identify the cause of failure, and maintenance action required to recover from the identified failure is conducted. There are limitations for such monitoring systems, as they identify the problem only after the fault has occurred. Therefore, present methods as described above generally are not able to predict and prevent turbine damage. Furthermore, due to inherent time delays associated with analyzing faults, determining failure causes, and identifying corrective action steps, use of present methods often results in undesirable lengths of repair time for critical turbine components.
In certain gas turbine monitoring devices, system modeling techniques use engine performance parameters to approximate thermodynamic processes within a gas turbine. In one such system, the engine pressure ratio (EPR) is monitored for a gas turbine engine and used as an independent variable to determine expected values of fuel flow, exhaust gas temperature, and rotational speed of the high pressure compressor stage for a properly operating theoretical engine of the type being monitored. Diode networks are arranged to effect voltage transfer characteristics that closely approximate the parametric relationship between EPR and one of the dependent variables such as, for example, fuel flow in the theoretical engine. The diode network provides expected values of operating parameters in an ideal condition. The signals from the actual engine and the expected values are supplied to an analog computational network to calculate deviation of the actual values from expected values as a means to detect anomalies. Such systems face a drawback in that they are not capable of precisely monitoring engine performance over the entire operation of the engine being monitored. Many systems modeling techniques unrealistically assume that the values of engine performance parameters remain constant at different operating conditions, and most systems do not take into account the gradual deterioration in performance during an engine's operating life.
In gas turbines, exhaust temperature monitoring is desirable since high temperatures can cause damage to combustor elements, hot gas path parts, rotor blades, and the like. High exhaust gas temperatures may also cause emission levels of certain regulated compounds, such as nitrogen oxides, to rise above allowable limits. Temperature sensors, such as, for example, thermocouples, have been used in prior art systems to determine the temperature of discharges gases in the turbine combustor. Though temperature monitoring provides information important to increased turbine reliability, this technique alone generally is not sufficient in the identification of particular components operating in, or at risk of operating in, an anomalous condition.
Therefore there is a need for an improved mechanism for monitoring the performance of a gas turbine system so that particular components causing anomalous operation may be identified and an informed prognosis may be made regarding the time the turbine can be operated until maintenance and repair procedures are required.